What Is AI Transformation and Why Your Business Needs It

In the wake of digital transformation, which has already reshaped the business landscape, companies now find themselves at the threshold of a new frontier — AI transformation. Much like its predecessor, which prompted organizations to rethink their strategies and adopt digital processes, AI transformation promises to improve the intelligence of those processes. When adopted correctly, it can streamline business operations with the help of large language models (LLMs), predictive analytics, and other AI subsets.

One of the latest Forbes Advisor surveys states that 64% of businesses believe AI will help increase their overall productivity. No wonder AI is expected to see an annual growth rate of 36.6% from 2024 to 2030, as reported by Grand View Research. These numbers indicate that AI will be a significant revolutionary element of the upcoming digital era.

In this article, we’ll share insights on why and how companies should use AI to stay competitive, what challenges may stand in their way, and real-life examples of using AI to boost companies’ operational efficiency and performance.

AI has rapidly become a transformative force, pushing businesses to rethink their strategies and operations. Leading tech companies like Amazon, Apple, Facebook, IBM, Google, and Microsoft are heavily investing in AI research and development, driving the growth of the AI market. Their efforts aim to make artificial intelligence, namely generative AI, more accessible for enterprise usage. 

The latest McKinsey Global Survey on AI states that 65% of organizations regularly use generative AI, which is twice the percentage in 2023. Compared with the previous year, companies have also started integrating AI into more areas of their business.

Let’s discover what is behind this fast adoption rate, how AI is changing business, and what benefits it can bring.

Companies have been using artificial intelligence long before the advent of Open AI’s famous NLP-based chatbot. However, its appearance on the market has catalyzed a new era of business transformation. As of mid-2024, generative AI has already proven effective in taking over some aspects of business activities.

An Accenture study reveals that 97% of global executives believe AI foundation models will revolutionize work by building human-AI collaboration and enabling connections across different data types. No wonder that 57% of IT leaders call generative AI a “game changer,” recognizing its potential to improve customer service, use data more effectively, and enhance operational efficiency.

These potential benefits are driving companies to prioritize generative AI for their business. In fact, a Salesforce survey of over 500 senior IT leaders found that 67% are focusing on adopting generative AI within the next 18 months, with 33% naming it a top priority.

Since OpenAI released version 3.5 of ChatGPT for public use in November 2022, companies and their employees have been exploring ways to use it for corporate purposes. As of June 2023, Statista reported that 10.8% of employees had used ChatGPT in the workplace at least once, and 4,7% had entered confidential corporate data into ChatGPT. The most commonly exposed type of corporate data was the sensitive data intended for internal use only.

While market giants like Apple, JPMorgan Chase, Northrop Grumman, and Samsung have restricted or completely banned GhatGPT usage at work, some companies, like Deutsche Bank, are exploring safe and compliant ways to use generative AI tools.

Developing custom LLMs can be the answer, allowing you to tailor AI capabilities to your company’s specific needs, tasks, domains, or datasets. When your company builds and uses custom LLMs, it controls how data is handled, stored, and processed. This means the company can enforce strict data protection policies and ensure that no sensitive information is shared outside the organization.

The potential impact LLM usage can have on various industries and job roles is significant. Accenture Research proves it by reporting that language tasks make up 62% of the total working time in the US, and 65% of these tasks have a high potential to be augmented by large language models. Based on these findings, Accenture predicts that LLMs could impact around 40% of working hours across industries.

Here’s an approximate work time distribution by industry and the potential impact AI can make.

Generative AI, with its advanced capabilities, extends the benefits of traditional AI models. Gen AI-powered systems can analyze large datasets, identify patterns, and provide actionable insights that conventional methods might miss.

Here are the main functions businesses expect when investing in AI transformation.

• Advising. Co-pilot and advanced AI tools like large language models (LLMs) can improve decision-making by offering insights and recommendations based on company data analysis. The latest Accenture study reveals that LLMs, for instance, can handle approximately 70% of complex customer service queries. These models can understand customer intent, formulate accurate and high-quality responses, and learn by interacting with customers.
• Creating. Companies can use AI generation tools like Stable Diffusion, Midjourney, Gemini, and ChatGPT to generate creative content, experiment with ideas, and augment their creative workflows. However, as of now, AI generation tools can complement human expertise rather than replacing it entirely. The major reasons are inaccuracy and intellectual property infringement risks associated with an organization’s generative AI usage. According to McKinsey’s The State of AI in Early 2024 survey, around 25% of organizations state they have already experienced negative consequences from generative AI’s inaccuracy.
• Coding. McKinsey’s empirical research finds that generative AI-based tools can significantly speed up many common developer tasks. For instance, documenting code functionality can be completed in half the time, while optimizing existing code can be done in about two-thirds of the time. This increase in productivity allows developers to focus more on high-complexity tasks where AI can barely help.

• Automating. Robotic Process Automation (RPA) bots have been handling repetitive and rule-based tasks for several years; however, generative AI can bring automation to a new level. It can take into account historical context, suggest the next best actions, and offer predictive intelligence. This advancement will allow organizations to automate more complex and nuanced tasks, improving efficiency and freeing human workers to focus on higher-level strategic activities.
• Protecting. Generative AI has the power to protect organizations against fraud and proactively identify risks. In strategic cyber defense, LLMs can explain malware and quickly classify websites. Additionally, AI can help ensure regulatory compliance by continuously monitoring and analyzing compliance-related data, reducing the likelihood of costly violations. According to a KPMG report on Generative AI usage in cybersecurity, 72% of IT professionals say that cybersecurity applications will be the top priority for generative AI adoption, with 64% planning to implement this strategy within the next six to twelve months.

Given the benefits, businesses are rightly optimistic about generative AI’s potential to transform work. However, they also need to consider the challenges that might come with fundamentally rethinking how their organization operates.

A recent Deloitte survey found that 56% of organizations already use generative AI to reduce operational costs and increase efficiency. Yet, despite the hype around the technology, many CEOs feel underprepared to integrate AI into their business strategies effectively. 

Here, we’ve gathered major AI transformation challenges that may hinder companies.

The success of AI models heavily depends on the quality of the data on which they are trained. Companies that have modernized their systems and invested in their data architecture in recent years are well-positioned to capture early value from AI adoption. In turn, inconsistent, incomplete, or biased data can lead to poor predictions or results. Ensuring data accuracy, relevance, and comprehensiveness is critical to getting the ball rolling, but it can be challenging, especially for organizations with large, diverse datasets.

The latest McKinsey “The State of AI” survey reveals that 70% of companies face difficulties preparing data. These difficulties include establishing data governance processes, quickly integrating data into AI models, and lacking a sufficient amount of training data.

Implementing AI solutions can be costly, requiring significant technology, infrastructure, and talent investment. According to McKinsey, 89% of large companies globally are undergoing digital transformations, but top performers only capture a median of 50% of the full revenue benefits from these efforts, compared to 25% of expected cost savings across all respondents. This raises the question: what ROI can you expect from AI transformation?

Generative AI’s quick wins often focus on automation and productivity improvements. For example, after just one month, Klarna’s AI assistant, powered by OpenAI, has already been handling two-thirds of its customer service chats. The ROI from AI transformation at scale will greatly depend on the use case, its complexity, and the organization’s ability to effectively integrate AI into its operations.

AI systems can inadvertently perpetuate or even strengthen existing biases in the data they are trained on. This often happens when the training data reflects historical prejudices or lacks diversity. Bias can also result from how the training data is labeled. For example, AI sales assistant tools that use inconsistent labeling and exclude or overrepresent specific customer characteristics could eliminate potential quality leads.

LLMs can sometimes generate convincing but incorrect or nonsensical outputs, a phenomenon known as AI hallucination. These hallucinations range from minor factual inaccuracies to entirely fabricated data, references, or stories. They occur due to learning errors, gaps in knowledge, or misinterpretations of the training data. This issue presents significant challenges for the practical deployment of LLMs and raises concerns about their reliability in real-world scenarios.

McKinsey states that the biggest challenge for SMBs and enterprises to successfully adopt generative AI is the talent gap. This includes employees’ lack of proficiency in using generative AI and the business, legal, and IT skills required to develop and refine generative AI applications.

We have hands-on experience using different LLMs (GPT 3.5+​, LLaMA ​, Gemini), generative models, computer vision, and AI API providers and tools to design, train, fine-tune, and integrate custom AI-powered solutions.

As a certified Microsoft Solutions Partner in Digital and App Innovation, we adopt its latest tech innovations early on, including the Azure AI tech stack. It covers ready-to-use managed AI services, including cognitive APIs, machine learning, and advanced conversational AI. Our proficiency in using and integrating Azure AI Services across various use cases helped us build AI-powered scalable solutions that boosted our customers’ operations.

Leobit has already acknowledged the power of AI in boosting company operations and transformed our internal processes by integrating custom AI-powered solutions. Once we developed and deployed the Leobit Corporate LLM, we could easily roll out the new solutions. Now, we have several AI employees built on top of Leobit corporate LLM:

• LeoSlack Leonard (Slack chatbot that answers HR-related questions)
• Leora (Leobit AI sales manager)
• Leo (AI sales email auto-response solution)

Leobit has a long track of secure, feature-rich applications powered by artificial intelligence. We can help you adopt AI, build your custom LLM solutions, or prepare data for fine-tuning AI/ML algorithms so they can properly analyze it.

Here are some recent AI-based projects we worked on.

Our customer, a global brand specializing in medical skin imaging systems, approached us to revamp the business logic and architecture of their existing solution and develop a new kiosk app. Leobit’s role in this project also included preparing data for the company’s deep-learning algorithm to ensure accurate analysis. 

To handle this task, we used a TensorFlow model capable of processing photos presented as bytes. Before undergoing analysis, we optimized images to align with the input format required by the AI model, a process facilitated by OpenCV. 

Additionally, we implemented an SDK plugin that allows the client to use another pre-trained ML model or enhance the existing one. We also refined the process to enable users to specify a mole’s location on the body, facilitating more precise AI analysis. Learn more about the project in our AI-Based Digital Dermoscopy Application case study.

Our customer, a Swiss high-tech company, aspired to transform the manufacturing industry by adopting AI to automate the quotation process for CNC manufacturers. The software used various ML algorithms connected to different libraries to analyze and calculate CNC production time.

Leobit developed services to retrieve, process, and transform information from ML services and construct the entire ML model training history for visualization. This allows manufacturers to visualize how their ML model underwent training and influence the results showcased on the website.

To improve the system’s performance, we suggested minimizing the ML model size for swift loading and unloading into ML services. We developed a service that groups models, archives them, and unloads them to the cloud based on the system’s workflow, significantly boosting performance.

In addition to managing ML models, our team also optimized cloud costs, built a location-based directory, and implemented the latest encryption technologies to ensure secure data handling. Check out our AI-powered SaaS for CNC manufacturers case study to learn more about the project and our contributions.

Leobit needed an internal solution to automate the initial sales qualification process, improve response time, enhance engagement, and increase the conversion rate. Using our expertise in AI/LLM, as well as custom software development, Leobit tackled this challenge under the leadership of CEO and Founder Oleksa Stelmakh. Within a week, Oleksa developed an initial Proof of Concept (PoC) using Chat GPT. The PoC architecture used Google Sheets as a central hub for categorization and scoring, with Google Apps Script handling integration and lead categorization.

To filter incoming queries, we established criteria based on the services and other company-specific factors. The AI-powered email auto-response solution generated responses using a combination of custom templates and AI-generated content. Initial responses were tailored to specific customer inquiries, showcasing Leobit’s relevant expertise, project case studies, and certifications.

After successfully testing the PoC, Leobit evolved the solution by integrating Azure OpenAI Service and developing a more sophisticated architecture, including:

• Transitioning from OpenAI GPT-4 API to Google Gemini and then migrating to Azure OpenAI Service for a custom fine-tuned model
• Expanding the knowledge base with more company-specific content
• Migrating logic from Google Sheets to C# and deploying it as Azure Functions

By integrating this custom LLM solution into the sales process, Leobit could send faster and more accurate responses to leads’ queries. Less than one hour after initial contact, customers began scheduling calls directly through AI-generated responses, which helped us achieve higher engagement and conversions. 

Explore our AI/LLM Sales Email Auto-Response Solution case study for more details.

AI adoption has already reached an inflection point, with companies investing in AI transformation outpacing those who don’t. No wonder, since the potential benefits of generative AI range from cutting down operational expenses and enhancing customer experiences to boosting security and driving innovation.

Organizations prioritizing AI transformation today are better positioned to open up new opportunities for growth and differentiation in the market. As the demand for AI solutions continues to rise, so do the challenges companies may face on their journey of AI transformation. Poor data quality and improper training may lead to AI hallucinations and bias. Fortunately, there are strategies to address these issues.

Partnering with an experienced company like Leobit can help you mitigate potential risks and build custom corporate LLM solutions. Our AI/LLM software development services can cover all your needs — from consulting to AI solution development, fine-tuning, and integration. Contact us to learn more.